System and method for detecting, tracking and counting human objects of interest using a counting system and a data capture device

ABSTRACT

A system for counting and tracking objects of interest within a predefined area with a sensor that captures object data and a data capturing device that receives subset data to produce reports that provide information related to a time, geographic, behavioral, or demographic dimension.

CROSS-REFERENCE

This application claims the benefit of U.S. Provisional PatentApplication No. 61/538,554 filed on Sep. 23, 2011 and U.S. ProvisionalPatent Application No. 61/549,511 filed on Oct. 20, 2011, which areincorporated herein in their entirety.

BACKGROUND

1. Field of the Invention

The present invention generally relates to the field of objectdetection, tracking, and counting. In specific, the present invention isa computer-implemented detection and tracking system and process fordetecting and tracking human objects of interest that appear in cameraimages taken, for example, at an entrance or entrances to a facility, aswell as counting the number of human objects of interest entering orexiting the facility tier a given time period.

2. Related Prior Art

Traditionally, various methods for detecting and counting the passing ofan object have been proposed. U.S. Pat. No. 7,161,482 describes anintegrated electronic article surveillance (EAS) and people countingsystem. The EAS component establishes an interrogatory zone by anantenna positioned adjacent to the interrogation zone at an exit pointof a protected area. The people counting component includes one peopledetection device to detect the passage of people through an associatedpassageway and provide a people detection signal, and another peopledetection device placed at a predefined distance from the first deviceand configured to detect another people detection signal. The twosignals are then processed into an output representative of a directionof travel in response to the signals.

Basically, there are two classes of systems employing video images forlocating and tracking human objects of interest. One class usesmonocular video streams or image sequences to extract, recognize, andtrack objects of interest. The other class makes use of two or morevideo sensors to derive range or height maps from multiple intensityimages and uses the range or height maps as a major data source.

In monocular systems, objects of interest are detected and tracked byapplying background differencing, or by adaptive template matching, orby contour tracking. The major problem with approaches using backgrounddifferencing is the presence of background clutters, which negativelyaffect robustness and reliability of the system performance. Anotherproblem is that the background updating rate is hard to adjust in realapplications. The problems with approaches using adaptive templatematching are:

1) object detections tend to drift from true locations of the objects,or get fixed to strong features in the background; and

2) the detections are prone to occlusion. Approaches using the contourtracking suffer from difficulty in overcoming degradation by intensitygradients in the background near contours of the objects. In addition,all the previously mentioned methods are susceptible to changes inlighting conditions, shadows, and sunlight.

In stereo or multi-sensor systems, intensity images taken by sensors areconverted to range or height maps, and the conversion is not affected byadverse factors such as lighting condition changes, strong shadow, orsunlight.

Therefore, performances of stereo systems are still very robust andreliable in the presence of adverse factors such as hostile lightingconditions. In addition, it is easier to use range or height informationfor segmenting, detecting, and tracking objects than to use intensityinformation.

Most state-of-the-art stereo systems use range background differencingto detect objects of interest. Range background differencing suffersfrom the same problems such as background clutter, as the monocularbackground differencing approaches, and presents difficulty indifferentiating between multiple closely positioned objects.

U.S. Pat. No. 6,771,818 describes a system and process of identifyingand locating people and objects of interest in a scene by selectivelyclustering blobs to generate “candidate blob clusters” within the sceneand comparing the blob clusters to a model representing the people orobjects of interest. The comparison of candidate blob clusters to themodel identifies the blob clusters that is the closest match or matchesto the model. Sequential live depth images may be captured and analyzedin real-time to provide for continuous identification and location ofpeople or objects as a function of time.

U.S. Pat. Nos. 6,952,496 and 7,092,566 are directed to a system andprocess employing color images, color histograms, techniques forcompensating variations, and a sum of match qualities approach to bestidentify each of a group of people and objects in the image of a scene.An image is segmented to extract regions which likely correspond topeople and objects of interest and a histogram is computed for each ofthe extracted regions. The histogram is compared with pre-computed modelhistograms and is designated as corresponding to a person or object ifthe degree of similarity exceeds a prescribed threshold. The designatedhistogram can also be stored as an additional model histogram.

U.S. Pat. No. 7,176,441 describes a counting system for counting thenumber of persons passing a monitor line set in the width direction of apath. A laser is installed for irradiating the monitor line with a slitray and an image capturing device is deployed for photographing an areaincluding the monitor line. The number of passing persons is counted onthe basis of one dimensional data generated from an image obtained fromthe photographing when the slit ray is interrupted on the monitor linewhen a person passes the monitor line.

Despite all the prior art in this field, no invention has developed atechnology that enables unobtrusive detection and tracking of movinghuman objects, requiring low budget and maintenance while providingprecise traffic counting results with the ability to distinguish betweenincoming and outgoing traffic, moving and static objects, and betweenobjects of different heights. Thus, it is a primary objective of thisinvention to provide an unobtrusive traffic detection, tracking, andcounting system that involves low cost, easy and low maintenance,high-speed processing, and capable of providing time-stamped resultsthat cat be further analyzed.

In addition, people counting systems typically create anonymous trafficcounts. In retail traffic monitoring, however, this may be insufficient.For example, some situations may require store employees to accompanycustomers through access points that are being monitored by an objecttracking and counting system, such as fitting rooms. In thesecircumstances, existing systems are unable to separately track and countemployees and customers. The present invention would solve thisdeficiency.

SUMMARY OF THE INVENTION

The present invention is directed to a system and process for detecting,tracking, and counting human objects of interest entering or exiting anentrance or entrances of a facility.

According to the present invention, the system includes: at least onesensor at the entrance to obtain data; a data capturing device forreceiving subset data; and a processor for matching object data andsubset data to provide information related to a time, geographic,behavioral, or demographic dimension.

An objective of the present invention is to provide a technique capableof achieving a reasonable computation load and providing real-timedetection, tracking, and counting results.

Another objective is to provide easy and unobtrusive tracking andmonitoring of the facility.

Another objective of the present invention is to provide a technique todetermine the ratio of the number of human objects entering the facilityover the number of human objects of interest passing within a certaindistance from the facility.

In accordance with these and other objectives that will become apparenthereafter, the present invention will be described with particularreferences to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic perspective view of a facility in which the systemof the present invention is installed;

FIG. 2 is a diagram illustrating the image capturing device connected toan exemplary counting system of the present invention;

FIG. 3 is a diagram illustrating the sequence of converting one or morestereo image pairs captured by the system of the present invention intothe height maps, which are analyzed to track and count human objects;

FIG. 4 is a flow diagram describing the flow of processes for a systemperforming human object detection, tracking, and counting according tothe present invention;

FIG. 5 is a flow diagram describing the flow of processes for objecttracking;

FIGS. 6A-B are a flow diagram describing the flow of processes for trackanalysis;

FIGS. 7A-B are a first part of a flow diagram describing the flow ofprocesses for suboptimal localization of unpaired tracks;

FIGS. 8A-B are a second part of the flow diagram of FIG. 7 describingthe flow of processes for suboptimal localization of impaired tracks;

FIGS. 9A-B are a flow diagram describing the flow of processes forsecond pass matching of tracks and object detects;

FIG. 10 is a flow diagram describing the flow of processes for trackupdating or creation;

FIG. 11 is a flow diagram describing the flow of processes for trackmerging;

FIG. 12 is a flow diagram describing the flow of processes for trackupdates;

FIG. 13 is a diagram illustrating the image capturing device connectedto an exemplary counting system, which includes an RFID reader;

FIG. 14 is a flow diagram depicting the flow of processes for retrievingobject data and tag data and generating track arrays and sequencearrays;

FIG. 15 is a flow diagram depicting the flow of processes fordetermining whether any overlap exists between any of the track recordsand any of the sequence records;

FIG. 16 is a flow diagram depicting the flow of processes for generatinga match record 316 for each group of sequence records whose trackrecords overlap;

FIG. 17 is a flow diagram depicting the flow of processes forcalculating the match quality scores;

FIG. 18A is a flow diagram depicting the flow of processes fordetermining which track record is the best match for a particularsequence;

FIG. 18B is a flow diagram depicting the flow of processes fordetermining the sequence record that holds the sequence record/trackrecord combination with the highest match quality score;

FIG. 19 is a diagram illustrating an image capturing device connected toan exemplary counting system, which also includes a data capturingdevice;

FIGS. 20A-B are a flow diagram depicting the steps for generatingreports based on a time dimension;

FIG. 21 is a flow diagram depicting the steps for generating reportsbased on a geographic dimension;

FIG. 22 is a diagram illustrating a multi-floor area;

FIGS. 23A-B are a flow diagram depicting the steps for generatingreports based on a behavioral dimension; and

FIG. 24 is a flow diagram depicting the steps for generating reportsbased on a demographic dimension.

DETAILED DESCRIPTION OF THE INVENTION

This detailed description is presented in terms of programs, datastructures or procedures executed on a computer or a network ofcomputers. The software programs implemented by the system may bewritten in languages such as JAVA, C, C++, C#, Assembly language,Python, PHP, or HTML. However, one of skill in the art will appreciatethat other languages may be used instead, or in combination with theforegoing.

1. System Components

Referring to FIGS. 1, 2 and 3, the present invention is a system 10comprising at least one image capturing device 20 electronically orwirelessly connected to a counting system 30. In the illustratedembodiment, the at least one image capturing device 20 is mounted abovean entrance or entrances 21 to a facility 23 for capturing images fromthe entrance or entrances 21. Facilities such as malls or stores withwide entrances often require more than one image capturing device tocompletely cover the entrances. The area captured by the image capturingdevice 20 is field of view 44. Each image, along with the time when theimage is captured, is a frame 48 (FIG. 3).

Typically, the image capturing device includes at least one stereocamera with two or more video sensors 46 (FIG. 2), which allows thecamera to simulate human binocular vision. A pair of stereo imagescomprises frames 48 taken by each video sensor 46 of the camera. Aheight map 56 is then constructed from the pair of stereo images throughcomputations involving finding corresponding pixels in rectified frames52, 53 of the stereo image pair.

Door zone 84 is an area in the height map 56 marking the start positionof an incoming track and end position of an outgoing track. Interiorzone 86 is an area marking the end position of the incoming track andthe start position of the outgoing track. Dead zone 90 is an area in thefield of view 44 that is not processed by the counting system 30.

Video sensors 46 (FIG. 2) receive photons through lenses, and photonscause electrons in the image capturing device 20 to react and form lightimages. The image capturing device 20 then converts the light images todigital signals through which the device 20 obtains digital raw frames48 (FIG. 3) comprising pixels. A pixel is a single point in a raw frame48. The raw frame 48 generally comprises several hundred thousands ormillions of pixels arranged in rows and columns.

Examples of video sensors 46 used in the present invention include CMOS(Complementary Metal-Oxide Semiconductor) sensors and/or CCD(Charge-Coupled Device) sensors. However, the types of video sensors 46should not be considered limiting, and any video sensor 46 compatiblewith the present system may be adopted.

The counting system 30 comprises three main components: (1) boot loader32; (2) system management and communication component 34; and (3)counting component 36.

The boot loader 32 is executed when the system is powered up and loadsthe main application program into memory 38 for execution.

The system management and communication component 34 includes taskschedulers, database interface, recording functions, and TCP/IP or PPPcommunication protocols. The database interface includes modules forpushing and storing data generated from the counting component 36 to adatabase at a remote site. The recording functions provide operationssuch as writing user defined events to a database, sending mails, andvideo recording.

The counting component 36 is a key component of the system 10 and isdescribed in further detail as follows.

2. The Counting Component.

In an illustrated embodiment of the present invention, at least oneimage capturing device 20 and the counting system 30 are integrated in asingle image capturing and processing device. The single image capturingand processing device can be installed anywhere above the entrance orentrances to the facility 23. Data output from the single imagecapturing and processing device can be transmitted through the systemmanagement and communication component 34 to the database for storageand further analysis.

FIG. 4 is a diagram showing the flow of processes of the countingcomponent 36. The processes are: (1) obtaining raw frames (block 100);(2) rectification (block 102); (3) disparity map generation (block 104);(4) height map generation (block 106); (5) object detection (block 108);and (6) object tracking (block 110).

Referring to FIGS. 1-4, in block 100, the image capturing device 20obtains raw image frames 48 (FIG. 3) at a given rate (such as for every1 As second) of the field of view 44 from the video sensors 46. Eachpixel in the raw frame 48 records color and light intensity of aposition in the field of view 44. When the image capturing device 20takes a snapshot, each video sensor 46 of the device 20 produces adifferent raw frame 48 simultaneously. One or more pairs of raw frames48 taken simultaneously are then used to generate the height maps 56 forthe field of view 44, as will be described.

When multiple image capturing devices 20 are used, tracks 88 generatedby each image capturing device 20 are merged before proceeding to block102.

Block 102 uses calibration data of the stereo cameras (not shown) storedin the image capturing device 20 to rectify raw stereo frames 48. Therectification operation corrects lens distortion effects on the rawframes 48. The calibration data include each sensor's optical center,lens distortion information, focal lengths, and the relative pose of onesensor with respect to the other. After the rectification, straightlines in the real world that have been distorted to curved lines in theraw stereo frames 48 are corrected and restored to straight lines. Theresulting frames from rectification are called rectified frames 52, 53(FIG. 3).

Block 104 creates a disparity map 50 (FIG. 3) from each pair ofrectified frames 52, 53. A disparity map 50 is an image map where eachpixel comprises a disparity value. The term disparity was originallyused to describe a 2-D vector between positions of correspondingfeatures seen by the left and right eyes. Rectified frames 52, 53 in apair are compared to each other for matching features. The disparity iscomputed as the difference between positions of the same feature inframe 52 and frame 51.

Block 106 converts the disparity map 50 to the height map 56. Each pixelof the height map 56 comprises a height value and x-y coordinates, wherethe height value is represented by the greatest ground height of all thepoints in the same location in the field of view 44. The height map 56is sometimes referred to as a frame in the rest of the description.

2.1 Object Detection

Object detection (block 108) is a process of locating candidate objects58 in the height map 56. One objective of the present invention is todetect human objects standing or walking in relatively flat areas.Because human objects of interest are much higher than the ground, localmaxima of the height map 56 often represent heads of human objects oroccasionally raised hands or other objects carried on the shoulders ofhuman objects walking in counting zone 84,86 (FIG. 1). Therefore, localmaxima of the height map 56 are identified as positions of potentialhuman object 58 detects. Each potential human object 58 detect isrepresented in the height map 56 by a local maximum with a heightgreater than a predefined threshold and all distances from other localmaxima above a predefined range.

Occasionally, some human objects of interest do not appear as localmaxima for reasons such as that the height map 56 is affected by falsedetection due to snow blindness effect in the process of generating thedisparity map 50, or that human objects of interests are standing closeto taller objects such as walls or doors. To overcome this problem, thecurrent invention searches in the neighborhood of the most recent localmaxima for a suboptimal location as candidate positions for humanobjects of interest, as will be described later.

A run is a contiguous set of pixels on the same row of the height map 56with the same non-zero height values. Each run is represented by afour-tuple (row, start-column, end-column, height). In practice, heightmap 56 is often represented by a set of runs in order to boostprocessing performance and object detection is also performed on theruns instead of the pixels.

Object detection comprises four stages: 1) background reconstruction; 2)first pass component detection; 3) second pass object detection; and 4)merging of closely located detects.

2.1.1 Component Definition and Properties

Pixel q is an eight-neighbor of pixel p if q and p share an edge or avertex in the height map 56, and both p and q have non-zero heightvalues. A pixel can have as many as eight-neighbors.

A set of pixels E is an eight-connected component if for every pair ofpixels Pi and Pi in E, there exist a sequence of pixels Pi′ . . . , Pisuch that all pixels in the sequence belong to the set E, and every pairof two adjacent pixels are eight neighbors to each other. Withoutfurther noting, an eight connected component is simply referred to as aconnected component hereafter.

The connected component is a data structure representing a set ofeight-connected pixels in the height map 56. A connected component mayrepresent one or more human objects of interest. Properties of aconnected component include height, position, size, etc. Table 1provides a list of properties associated with a connected component.Each property has an abbreviated name enclosed in a pair of parenthesesand a description. Properties will be referenced by their abbreviatednames hereafter.

TABLE 1 Variable Name Number (abbreviated name) Description 1 componentID (det_ID) Identification of a component. In the first pass,componentID represents the component. In the second pass, componentIDrepresents the parent component from which the current component isderived. 2 peak position (det_maxX, det_maxY) Mass center of the pixelsin the component having the greatest height value. 3 peak area(det_maxArea) Number of pixels in the component having the greatestheight value. 4 center (det_X, det_Y) Mass center of all pixels of thecomponent. 5 minimum size Size of the shortest side of two minimum(def_minSize) rectangles that enclose the component at 0 and 45 degrees.6 maximum size Size of the longest side of two minimum (det_maxSize)rectangles that enclose the component at 0 and 45 degrees. 7 area(det_area) Number of pixels of the component. 8 minimum height Minimumheight of all pixels of the (det_minHeight) component. 9 maximum heightMaximum height of all pixels of the (det_maxHeight) component. 10 heightsum (det_htSum) Sum of heights of pixels in a small square windowcentered at the center position of the component, the window having aconfigurable size. 11 Grouping flag A flag indicating whether thesubcomponent de_grouped) still needs grouping. 12 background A flagindicating whether the mass center of (det_inBackground) the componentis in the background 13 the closest detection Identifies a second passcomponent closest to (det_closestDet) the component but remainingseparate after operation of “merging close detections”.

Several predicate operators are applied to a subset of properties of theconnected component to check if the subset of properties satisfies acertain condition. Component predicate operators include:

IsNoisy, which checks whether a connected component is too small to beconsidered a valid object detect 58. A connected component is consideredas “noise” if at least two of the following three conditions hold: 1)its det_minSize is less than two thirds of a specified minimum humanbody size, which is configurable in the range of [9,36] inches; 2) itsdet_area is less than four ninths of the area of a circle with itsdiameter equal to a specified minimum body size; and 3) the product ofits det_minSize and det area is less than product of the specifiedminimum human body size and a specified minimum body area.

IsPointAtBoundaries, which checks whether a square window centered atthe current point with its side equal to a specified local maximumsearch window size is intersecting boundaries of the height map 56, orwhether the connected component has more than a specific number ofpixels in the dead zone 90. If this operation returns true, the pointbeing checked is considered as within the boundaries of the height map56.

NotSmaliSubComponent, which checks if a subcomponent in the second passcomponent detection is not small. It returns true if its detrninxize isgreater than a specified minimum human head size or its det_area isgreater than a specified minimum human head area.

BigSubComponentSeed, which checks if a subcomponent seed in the secondpass component detection is big enough to stop the grouping operation.It returns true if its detrninxize is greater than the specified maximumhuman head size or its det_area is greater than the specified maximumhuman head area.

SmallSubComponent, which checks if a subcomponent in the second passcomponent detection is small. It returns true if its detrninxize is lessthan the specified minimum human head size or its der area is less thanthe specified minimum human head area.

2.1.2 Background Reconstruction

The background represents static scenery in the field view 44 of theimage capturing device 20 and is constructed from the height map 56. Thebackground building process monitors every pixel of every height map 56and updates a background height map. A pixel may be considered as partof the static scenery if the pixel has the same non-zero height valuefor a specified percentage of time (e.g., 70%).

2.1.3 First-Pass Component Detection

First pass components are computed by applying a variant of aneight-connected image labeling algorithm on the runs of the height map56. Properties of first pass components are calculated according to thedefinitions in Table 1. Predicate operators are also applied to thefirst pass components. Those first pass components whose “IsNoise”predicate operator returns “true” are ignored without being passed on tothe second pass component detection phase of the object detection.

2.1.4 Second Pass Object Detection

In this phase, height map local maxima, to be considered as candidatehuman detects, are derived from the first pass components in thefollowing steps.

First, for each first pass component, find all eight connectedsubcomponents whose pixels have the same height. The deigrouped propertyof all subcomponents is cleared to prepare for subcomponent grouping andthe deCID property of each subcomponent is set the ID of thecorresponding first pass component.

Second, try to find the highest ungrouped local maximal subcomponentsatisfying the following two conditions: (1) the subcomponent has thehighest height among all of the ungrouped subcomponents of the givenfirst pass component, or the largest area among all of the ungroupedsubcomponents of the given first pass component if several ungroupedsubcomponents with the same highest height exist; and (2) thesubcomponent is higher than all of its neighboring subcomponents. Ifsuch a subcomponent exists, use it as the current seed and proceed tothe next step for further subcomponent grouping. Otherwise, return tostep 1 to process the next first pass component in line.

Third, if BigSubComponentSeed test returns true on the current seed, thesubcomponent is then considered as a potential human object detect. Setthe dot grouped flag of the subcomponent to mark it as grouped andproceed to step 2 to look for a new seed. If the test returns false,proceed to the next step.

Fourth, try to find subcomponent next to the current seed that has thehighest height and meets all of the following three conditions: (1) itis eight-connected to the current seed; (2) its height is smaller thanthat of the current seed; and (3) it is not connected to a thirdsubcomponent that is higher and it passes the NotSmallSubComponent test.If more than one subcomponent meets all of above conditions, choose theone with the largest area. When no subcomponent meets the criteria, setthe deigrouped property of the current seed to “grouped” and go to step2. Otherwise, proceed to the next step.

Fifth, calculate the distance between centers of the current seed andthe subcomponent found in the previous step. If the distance is lessthan the specified detection search range or the current seed passes theSmallSubComponent test, group the current seed and the subcomponenttogether and update the properties of the current seed accordingly.Otherwise, set the det_grouped property of the current seed as“grouped”. Return to step 2 to continue the grouping process until nofurther grouping can be done.

2.1.5 Merging Closely Located Detections

Because the image capturing device 20 is mounted on the ceiling of thefacility entrance (FIG. 1), a human object of interest is identified bya local maximum in the height map. Sometimes more than one local maximadetection is generated from the same human object of interest. Forexample, when a human object raises both of his hands at the same time,two closely located local maxima may be detected. Therefore, it isnecessary to merge closely located local maxima.

The steps of this phase are as follows.

First, search for the closest pair of local maxima detections. If thedistance between the two closest detections is greater than thespecified detection merging distance, stop and exit the process.Otherwise, proceed to the next step.

Second, check and process the two detections according to the followingconditions in the given order. Once one condition is met, ignore theremaining conditions and proceed to the next step:

a) if either but not all detection is in the background, ignore the onein the background since it is most likely a static object (the localmaximum in the foreground has higher priority over the one in thebackground);

b) if either but not all detection is touching edges of the height map56 or dead zones, delete the one that is touching edges of the heightmap 56 or dead zones (a complete local maximum has higher priority overan incomplete one);

c) if the difference between det maxHeights of detections is smallerthan a specified person height variation threshold, delete the detectionwith significantly less 3-D volume (e.g., the product of det_maxHeightand det_masArea for one connected component is less than two thirds ofthe product for the other connected component) (a strong local maximumhas higher priority over a weak one);

d) if the difference between maximum heights of detections is more thanone foot, delete the detection with smaller det_maxHeight if thedetection with greater height among the two is less than the specifiedmaximum person height, or delete the detection with greaterdet_maxHeight if the maximum height of that detection is greater thanthe specified maximum person height (a local maxima with a reasonableheight has higher priority over a local maximum with an unlikelyheight);

e) delete the detection whose det area is twice as small as the other (asmall local maximum close to a large local maximum is more likely apepper noise);

f) if the distance between the two detections is smaller than thespecified detection search range, merge the two detections into one(both local maxima are equally good and close to each other);

g) keep both detections if the distance between the two detections islarger than or equal to the specified detection search range (both localmaxima are equally good and not too close to each other). Update thedet., closestDet attribute for each detection with the other detection'sID.

Then, return to step 1 to look for the next closest pair of detections.

The remaining local maxima detections after the above merging processare defined as candidate object detects 58, which are then matched witha set of existing tracks 74 for track extension, or new track initiationif no match is found.

2.2 Object Tracking

Object tracking (block 110 in FIG. 1) uses objects detected in theobject detection process (block 108) to extend existing tracks 74 orcreate new tracks 80. Some short, broken tracks are also analyzed forpossible track repair operations.

To count human objects using object tracks, zones 82 are delineated inthe height map 56. Door zones 84 represent door areas around thefacility 23 to the entrance. Interior zones 86 represent interior areasof the facility. A track 76 traversing from the door zone 84 to theinterior zone 86 has a potential “in” count. A track 76 traversing tothe door zone 84 from the interior zone 86 has a potential “out” count.If a track 76 traverses across zones 82 multiple times, there can beonly one potential “in” or “out” count depending on the direction of thelatest zone crossing.

As illustrated in FIG. 5, the process of object tracking 110 comprisesthe following phases: 1) analysis and processing of old tracks (block120); 2) first pass matching between tracks and object detects (block122); 3) suboptimal localization of unpaired tracks (block 124); 4)second pass matching between tracks and object detects (block 126); and5) track updating or creation (block 128).

An object track 76 can be used to determine whether a human object isentering or leaving the facility, or to derive properties such as movingspeed and direction for human objects being tracked.

Object tracks 76 can also be used to eliminate false human objectdetections, such as static signs around the entrance area. If an objectdetect 58 has not moved and its associated track 76 has been static fora relatively long time, the object detect 58 will be considered as partof the background and its track 76 will be processed differently thannormal tracks (e.g., the counts created by the track will be ignored).

Object tracking 110 also makes use of color or gray level intensityinformation in the frames 52, 53 to search for best match between tracks76 and object detects 58. Note that the color or the intensityinformation is not carried to disparity maps 50 or height maps 56.

The same technique used in the object tracking can also be used todetermine how long a person stands in a checkout line.

2.2.1 Properties of Object Track

Each track 76 is a data structure generated from the same object beingtracked in both temporal and spatial domains and contains a list of4-tuples (x, y, t, h) in addition to a set of related properties, whereh, x and y present the height and the position of the object in thefield of view 44 at time t. (x, y, h) is defined in a world coordinatesystem with the plane formed by x and y parallel to the ground and the haxis vertical to the ground. Each track can only have one position atany time. In addition to the list of 4-tuples, track 76 also has a setof properties as defined in Table 2 and the properties will be referredto later by their abbreviated names in the parentheses:

TABLE 2 Number Variable Name Description 1 ID number (trk_ID) A uniquenumber identifying the track. 2 track state (trk_state) A track could bein one of three states: active, inactive and deleted. Being active meansthe track is extended in a previous frame, being inactive means thetrack is not paired with a detect in a previous frame, and being deletedmeans the track is marked for deletion. 3 start point (trk_start) Theinitial position of the track (Xs, Ys, Ts, Hs). 4 end point (trk_end)The end position of the track (Xe, Ye, Te, He). 5 positive Step Numbers(trk_posNum) Number of steps moving in the same direction as theprevious step. 6 positive Distance (trk_posDist) Total distance bypositive steps. 7 negative Step Numbers (trk_negNum) Number of stepsmoving in the opposite direction to the previous step. 8 negativeDistance (trk_negDist) Total distance by negative steps. 9 backgroundcount The accumulative duration of the track in (trk_backgroundCount)background. 10 track range (trk_range) The length of the diagonal of theminimal rectangle covering all of the track's points. 11 start zone(trk_startZone) A zone number representing either door zone or interiorzone when the track is created. 12 last zone (trk_lastZone) A zonenumber representing the last zone the track was in. 13 enters(trk_enters) Number of times the track goes from a door zone to aninterior zone. 14 exits (trk_exits) Number of times the track goes froman interior zone to a door zone. 15 total steps (trk_totalSteps) Thetotal non-stationary steps of the track. 16 high point steps(trk_highPtSteps) The number of non-stationary steps that the track hasabove a maximum person height (e.g. 85 inches). 17 low point steps(trk_lowPtSteps) The number of non-stationary steps below a specifiedminimumn person height. 18 maximum track height The maximum height ofthe track. (trk_maxTrackHt) 19 non-local maximum detection point Theaccumulative duration of the time that the (trk_nonMaxDetNum) track hasfrom non-local maximum point in the height map and that is closest toany active track. 20 moving vector (trk_movingVec) The direction andoffset from the closest point in time to the current point with theoffset greater than the minimwn body size. 21 following track(trk_followingTrack) The ID of the track that is following closely. Ifthere is a track following closely, the distance between these twotracks don't change a lot, and the maximum height of the front track isless than a specified height for shopping carts, then the track in thefront may be considered as made by a shopping cart. 22 minimum followingdistance The minimum distance from this track to the(trk_minFollowingDist) following track at a point of time. 23 maximumfollowing distance The maximum distance from this track to the(trk_maxFollowingDist) following track at a point of time. 24 followingduration (trk_voteFollowing) The time in frames that the track isfollowed by the track specified in trk_followingTrack. 25 most recenttrack The id of a track whose detection t was once(trk_lastCollidingTrack) very close to this track's non-local minimumcandidate extending position. 26 number of merged tracks The number ofsmall tracks that this track is (trk_mergedTracks) made of throughconnection of broken tracks. 27 number of small track searches Thenumber of small track search ranges used (trk_smallSearches) in mergingtracks. 28 Mirror track (trk_mirrorTrack) The ID of the track that isvery close to this track and that might be the cause of this track. Thistrack itself has to be from a non-local maximum detection created by ablind search, or its height has to be less than or equal to thespecified minimum person height in order to be qualified as a candidatefor false tracks. 29 Mirror track duration The time in frames that thetrack is a candidate (trk_voteMirrorTrack) for false tracks and isclosely accompanied by the track specified in trk_mirrorTrack within adistance of the specified maximum person width. 30 Maximum mirror trackdistance The maximum distance between the track and (trk_maxMirrorDist)the track specified in trk_mirrorTrack.

2.2.2 Track-Related Predicative Operations

Several predicate operators are defined in order to obtain the currentstatus of the tracks 76. The predicate operators are applied to a subsetof properties of a track 76 to check if the subset of propertiessatisfies a certain condition. The predicate operators include:

IsNoisyNow, which checks if track bouncing back and forth locally at thecurrent time. Specifically, a track 76 is considered noisy if the trackpoints with a fixed number of frames in the past (specified as noisytrack duration) satisfies one of the following conditions:

a) the range of track 76 (trkrange) is less than the specified noisytrack range, and either the negative distance (trk_negDist) is largerthan two thirds of the positive distance (trk_posDist) or the negativesteps (trk_negNum) are more than two thirds of the positive steps(trk_posNum);

b) the range of track 76 (trkrange) is less than half of the specifiednoisy track range, and either the negative distance (trk_negDist) islarger than one third of the positive distance (trk_posDist) or thenegative steps (trk_negNum) are more than one third of the positivesteps (trk_posNum).

WholeTrackIsNoisy: a track 76 may be noisy at one time and not noisy atanother time.

This check is used when the track 76 was created a short time ago, andthe whole track 76 is considered noisy done of the following conditionsholds:

a) the range of track 76 (trkrange) is less than the specified noisytrack range, and either the negative distance (trk_negDist) is largerthan two thirds of the positive distance (trk_posDist) or the negativesteps (trk_negNum) are more than two thirds of the positive steps(trk_posNum);

b) the range of track 76 (trkrange) is less than half the specifiednoisy track range, and either the negative distance trk_negDist) islarger than one third of the positive distance (trk_posDist) or thenegative steps (trk_negNum) are more than one third of the positivesteps (trk_posNum).

IsSameTrack, which check if two tracks 76, 77 are likely caused by thesame human object. All of the following three conditions have to be metfor this test to return true: (a) the two tracks 76, 77 overlap in timefor a minimum number of frames specified as the maximum track timeout;(b) the ranges of both tracks 76, 77 are above a threshold specified asthe valid counting track span; and (c) the distance between the twotracks 76, 77 at any moment must be less than the specified minimumperson width.

IsCountIgnored: when the track 76 crosses the counting zones, it may notbe created by a human object of interest. The counts of a track areignored if one of the following conditions is met:

Invalid Tracks: the absolute difference between trk_exits and trk_entersis not equal to one.

Small Tracks: trkrange is less than the specified minimum counting tracklength.

Unreliable Merged Tracks: trkrange is less than the specified minimumbackground counting track length as well as one of the following:trk_mergedTracks is equal to trk_smallSearches, or trk_backgroundCountis more than 80% of the life time of the track 76, or the track 76crosses the zone boundaries more than once.

High Object Test: trk_highPtSteps is larger than half of trk_totalSteps.

Small Child Test: trk_lowPtSteps is greater than ¾ of trk_totalSteps,and trk_maxTrackHt is less than or equal to the specified minimum personheight.

Shopping Cart Test: trk_voteFollowing is greater than 3,trk_minFollowingDist is more than or equal to 80% oftrk_maxFollowingDist, and trk_maxTrackHt is less than or equal to thespecified shopping cart height.

False Track test: trk_voteMirrorTrack is more than 60% of the life timeof the track 76, and trk_maxMirrorTrackDist is less than two thirds ofthe specified maximum person width or trk_totalVoteMirrorTrack is morethan 80% of the life time of the track 76.

2.2.3 Track Updating Operation

Referring to FIG. 12, each track 76 is updated with new information onits position, time, and height when there is a best matching humanobject detect 58 in the current height map 56 for First, set trk_stateof the track 76 to 1 (block 360).

Second, for the current frame, obtain the height by using median filteron the most recent three heights of the track 76 and calculate the newposition 56 by averaging on the most recent three positions of the track76 (block 362).

Third, for the current frame, check the noise status using trackpredicate operator IsNoisyNow. If true, mark a specified number offrames in the past as noisy. In addition, update noise relatedproperties of the track 76 (block 364).

Fourth, update the span of the track 76 (block 366).

Fifth, if one of the following conditions is met, collect the countcarried by track 76 (block 374):

a) the track 76 is not noisy at the beginning, but it has been noisy forlonger than the specified stationary track timeout (block 368); or

b) the track 76 is not in the background at the beginning, but it hasbeen in the background for longer than the specified stationary tracktimeout (block 370).

Finally, update the current zone information (block 372).

2.2.4 Track Prediction Calculation

It helps to use a predicted position of the track 76 when looking tierbest matching detect 58. The predicted position is calculated by linearextrapolation on positions of the track 76 in the past three seconds.

2.2.5 Analysis and Processing of Old Track

This is the first phase of object tracking. Active tracks 88 are tracks76 that are either created or extended with human object detects 58 inthe previous frame. When there is no best matching human object detect58 for the track 76, the track 76 is considered as inactive.

This phase mainly deals with tracks 76 that are inactive for a certainperiod of time or are marked for deletion in previous frame 56. Trackanalysis is performed on tracks 76 that have been inactive for a tongtime to decide whether to group them with existing tracks 74 or to markthem for deletion in the next frame 56. Tracks 76 are deleted if thetracks 76 have been marked for deletion in the previous frame 56, or thetracks 76 are inactive and were created a very short period of timebefore. If the counts of the soon-to-be deleted tracks 76 shall not beignored according to the IsCountIgnored predicate operator, collect thecounts of the tracks 76.

2.2.6 First Pass Matching Between Tracks and Detects

After all tracks 76 are analyzed for grouping or deletion, this phasesearches for optimal matches between the human object detects 58 (i.e.the set of local maxima found in the object detection phase) and tracks76 that have not been deleted.

First, check every possible pair of track 76 and detect 58 and put thepair into a candidate list if all of the following conditions are met:

1) The track 76 is active, or it must be long enough (e.g. with morethan three points), or it just became inactive a short period of timeago (e.g. it has less than three frames);

2) The smaller of the distances from center of the detect 58 to the lasttwo points of the track 76 is less than two thirds of the specifieddetection search range when the track 76 hasn't moved very far (e.g. thespan of the track 76 is less than the specified minimum human head sizeand the track 76 has more than 3 points);

3) If the detect 58 is in the background, the maximum height of thedetect 58 must be greater than or equal to the specified minimum personheight;

4) If the detect 58 is neither in the background nor close to dead zonesor height map boundaries, and the track 76 is neither in the backgroundnor is noisy in the previous frame, and a first distance from the detect58 to the predicted position of the track 76 is less than a seconddistance from the detect 58 to the end position of the track 76, use thefirst distance as the matching distance. Otherwise, use the seconddistance as the matching distance. The matching distance has to be lessthan the specified detection search range;

5) The difference between the maximum height of the detect 58 and theheight oblast point of the track 76 must be less than the specifiedmaximum height difference; and

6) If either the last point off-track 76 or the detect 58 is in thebackground, or the detect 58 is close to dead zones or height mapboundaries, the distance from the track 76 to the detect 58 must be lessthan the specified background detection search range, which is generallysmaller than the threshold used in condition (4).

Sort the candidate list in terms of the distance from the detect 58 tothe track 76 or the height difference between the detect 58 and thetrack 76 (if the distance is the same) in ascending order.

The sorted list contains pairs of detects 58 and tracks 76 that are notpaired. Run through the whole sorted list from the beginning and checkeach pair. If either the detect 58 or the track 76 of the pair is marked“paired” already, ignore the pair. Otherwise, mark the detect 58 and thetrack 76 of the pair as “paired”. [0144] 12.7 Search of SuboptimalLocation For Unpaired Tracks

Due to sparseness nature of the disparity map 50 and the height map 56,some human objects may not generate local maxima in the height map 56and therefore may be missed in the object detection process 108. Inaddition, the desired local maxima might get suppressed by a neighboringhigher local maximum from a taller object. Thus, some human objecttracks 76 may not always have a corresponding local maximum in theheight map 56. This phase tries to resolve this issue by searching for asuboptimal location for a track 76 that has no corresponding localmaximum in the height map 56 at the current time. Tracks 76 that havealready been paired with a detect 58 in the previous phase might gothrough this phase too to adjust their locations if the distance betweenfrom end of those tracks to their paired detects is much larger thantheir steps in the past. In the following description, the track 76currently undergoing this phase is called Track A. The search isperformed in the following steps.

First, referring to FIG. 7, if Track A is deemed not suitable for thesuboptimal location search operation (i.e., it is inactive, or it's inthe background, or it's close to the boundary of the height map 56 ordead zones, or its height in last frame was less than the minimum personheight (block 184)), stop the search process and exit. Otherwise,proceed to the next step.

Second, if Track A has moved a few steps (block 200) (e.g., three steps)and is paired with a detection (called Detection A) (block 186) that isnot in the background and whose current step is much larger than itsmaximum moving step within a period of time in the past specified by atrack time out parameter (block 202,204), proceed to the next step.Otherwise, stop the search process and exit.

Third, search around the end point of Track A in a range defined by itsmaximum moving steps for a location with the largest height sum in apredefined window and call this location Best Spot A (block 188). Ifthere are some detects 58 deleted in the process of merging of closelylocated detects in the object detection phase and Track A is long ineither the spatial domain or the temporal domain (e.g. the span of TrackA is greater than the specified noisy track span threshold, or Track Ahas more than three frames) (block 190), find the closest one to the endpoint of Track too. If its distance to the end point of Track A is lessthan the specified detection search range (block 206), search around thedeleted component for the position with the largest height sum and callit Best Spot AI (block 208). If neither Best Spot A nor Best Spot AIexists, stop the search process and exit. If both Best Spot A and BestSpot AI exist, choose the one with larger height sum. The best spotselected is called suboptimal location for Track A. If the maximumheight at the suboptimal location is greater than the predefined maximumperson height (block 192), stop the search and exit. If there is nocurrent detection around the suboptimal location (block 194), create anew detect 58 (block 214) at the suboptimal location and stop thesearch. Otherwise, find the closest detect 58 to the suboptimal locationand call it Detection B (block 196). If Detection B is the samedetection as Detection A in step 2 (block 198), update Detection A'sposition with the suboptimal location (block 216) and exit the search.Otherwise, proceed to the next step.

Fourth, referring to FIG. 8, if Detection B is not already paired with atrack 76 (block 220), proceed to the next step. Otherwise, call thepaired track of the Detection B as Track B and perform one of thefollowing operations in the given order before exiting the search:

1) When the suboptimal location for Track A and Detection B are from thesame parent component (e.g. in the support of the same first passcomponent) and the distance between Track A and Detection B is less thanhalf of the specified maximum person width, create a new detect 58 atthe suboptimal location (block 238) if all of the following threeconditions are met: (i) the difference between the maximum heights atthe suboptimal location and Detection B is less than a specified personheight error range; (ii) the difference between the height sums at thetwo locations is less than half of the greater one; (iii) the distancebetween them is greater than the specified detection search range andthe trk_range values of both Track A and Track B are greater than thespecified noisy track offset. Otherwise, ignore the suboptimal locationand exit;

2) If the distance between the suboptimal location and Detection B isgreater than the specified detection search range, create a new detect58 at the suboptimal location and exit;

3) If Track A is not sizable in both temporal and spatial domains (block226), ignore the suboptimal location;

4) If Track B is not sizable in both temporal and spatial domain (block228), detach Track B from Detection B and update Detection B's positionwith the suboptimal location (block 246). Mark Detection B as Track A'sclosest detection;

5) Look for best spot for Track B around its end position (block 230).If the distance between the best spot for Track B and the suboptimallocation is less than the specified detection search range (block 232)and the best spot for Track B has a larger height sum, replace thesuboptimal location with the best spot for Track B (block 233). If thedistance between is larger than the specified detection search range,create a detect 58 at the best spot for Track B (block 250). UpdateDetection A's location with the suboptimal location if Detection Aexists.

Fifth, if the suboptimal location and Detection B are not in the supportof the same first pass component, proceed to the next step. Otherwisecreate a new detection at the suboptimal location if their distance islarger than half of the specified maximum person width, or ignore thesuboptimal location and mark Detection B as Track A's closest detectionotherwise.

Finally, create a new detect 58 at suboptimal location and markDetection B as Track As closest detection (block 252) if their distanceis larger than the specified detection search range. Otherwise, updateTrack A's end position with the suboptimal location (block 254) if theheight sum at the suboptimal location is greater than the height sum atDetection B, or mark Detection Bas Track A's closest detectionotherwise.

2.2.8 Second Pass Matching Between Tracks and Detects

After the previous phase, a few new detections may be added and somepaired detects 72 and tracks 76 become unpaired again. This phase looksfor the optimal match between current unpaired detects 72 and tracks 76as in the following steps.

For every pair of track 76 and detect 58 that remain unpaired, put thepair into a candidate list if all of the following five conditions aremet:

1) the track 76 is active (block 262 in FIG. 9);

2) the distance from detect 58 to the end point of the track 76 (block274) is smaller than two thirds of the specified detection search range(block 278) when the track doesn't move too far (e.g. the span of thetrack 76 is less than the minimal head size and the track 76 has morethan three points (block 276));

3) if the detect 58 is in the background (block 280), the maximum heightof the detect 58 must be larger than or equal to the specified minimumperson height (block 282);

4) the difference between the maximum height and the height of the lastpoint of the track 76 is less than the specified maximum heightdifference (block 284);

5) the distance from the detect 58 to the track 76 must be smaller thanthe specified background detection search range, if either the lastpoint of the track 76 or the detect 58 is in background (block 286), orthe detect 58 is close to dead zones or height map boundaries (block288); or if not, the distance from the detect 58 to the track 76 must besmaller than the specified detection search range (block 292).

Sort the candidate list in terms of the distance from the detect 58 tothe track 76 or the height difference between the two (if distance isthe same) in ascending order (block 264).

The sorted list contains pairs of detects 58 and tracks 76 which are notpaired at all at the beginning. Then run through the whole sorted listfrom the beginning and check each pair. If either the detect 58 or thetrack 76 of the pair is marked “paired” already, ignore the pair.Otherwise, mark the detect 58 and the track 76 of the pair as “paired”(block 270).

2.2.9 Track Update or Creation

After the second pass of matching, the following steps are performed toupdate old tracks or to create new tracks:

First, referring to FIG. 10, for each paired set of track 76 and detect58 the track 76 is updated with the information of the detect 58 (block300,302).

Second, create a new track 80 for every detect 58 that is not matched tothe track 76 if the maximum height of the detect 58 is greater than thespecified minimum person height, and the distance between the detect 58and the closest track 76 of the detect 58 is greater than the specifieddetection search range (block 306,308). When the distance is less thanthe specified detection merge range and the detect 58 and the closesttrack 76 are in the support of the same first pass component (i.e., thedetect 58 and the track 76 come from the same first pass component), setthe trk_lastCollidingTrack of the closest track 76 to the ID of thenewly created track 80 if there is one (block 310,320).

Third, mark each unpaired track 77 as inactive (block 324). If thattrack 77 has a marked closest detect and the detect 58 has a pairedtrack 76, set the trk_lastCollidingTrack property of the current track77 to the track ID of the paired track 76 (block 330).

Fourth, for each active track 88, search for the closest track 89 movingin directions that are at most thirty degrees from the direction of theactive track 88. If the closest track 89 exists, the track 88 isconsidered as closely followed by another track, and “Shopping CartTest” related properties of the track 88 are updated to prepare for“Shopping Cart Test” when the track 88 is going to be deleted later(block 334).

Finally, for each active track 88, search for the closest track 89. Ifthe distance between the two is less than the specified maximum personwidth and either the track 88 has a marked closest detect or its heightis less than the specified minimum person height, the track 88 isconsidered as a less reliable false track. Update “False Track” relatedproperties to prepare for the “False Track” test later when the track 88is going to be deleted later (block 338).

As a result, all of the existing tracks 74 are either extended or markedas inactive, and new tracks 80 are created.

2.2.10 Track Analysis

Track analysis is applied whenever the track 76 is going to be deleted.The track 76 will be deleted when it is not paired with any detect for aspecified time period. This could happen when a human object moves outof the field view 44, or when the track 76 is disrupted due to poordisparity map reconstruction conditions such as very low contrastbetween the human object and the background.

The goal of track analysis is to find those tracks that are likelycontinuations of some soon-to-be deleted tracks, and merge them. Trackanalysis starts from the oldest track and may be applied recursively onnewly merged tracks until no tracks can be further merged. In thefollowing description, the track that is going to be deleted is called aseed track, while other tracks are referred to as current tracks. Thesteps of track analysis are as follows:

First, if the seed track was noisy when it was active (block 130 in FIG.6), or its trkrange is less than a specified merging track span (block132), or its trk_lastCollidingTrack does not contain a valid track IDand it was created in less than a specified merging track time periodbefore (block 134), stop and exit the track analysis process.

Second, examine each active track that was created before the specifiedmerging track time period and merge an active track with the seed trackif the “Is the Same Track” predicate operation on the active track(block 140) returns true.

Third, if the current track satisfies all of the following three initialtesting conditions, proceed to the next step. Otherwise, if there existsa best fit track (definition and search criteria for the best fit trackwill be described in forthcoming steps), merge the best fit track withthe seed track (block 172, 176). If there is no best fit track, keep theseed track if the seed track has been merged with at least one track inthis operation (block 178), or delete the seed track (block 182)otherwise. Then, exit the track analysis.

The initial testing conditions used in this step are: (1) the currenttrack is not marked for deletion and is active long enough (e.g. morethan three frames) (block 142); (2) the current track is continuous withthe seed track (e.g. it is created within a specified maximum tracktimeout of the end point of the seed track) (block 144); (3) if bothtracks are short in space (e.g., the trkrange properties of both tracksare less than the noisy track length threshold), then both tracks shouldmove in the same direction according to the relative offset of thetrk_start and trk_end properties of each track (block 146).

Fourth, merge the seed track and the current track (block 152). Returnto the last step if the current track has collided with the seed track(i.e., the trk_lastCollidingTrack of the current track is the trk_ID ofthe seed track). Otherwise, proceed to the next step.

Fifth, proceed to the next step if the Mowing two conditions are met atthe same time, otherwise return to step 3: (1) if either track is at theboundaries according to the “is at the boundary” checking (block 148),both tracks should move in the same direction; and (2) at least onetrack is not noisy at the time of merging (block 150). The noisycondition is determined by the “is noisy” predicate operator.

Sixth, one of two thresholds coming up is used in distance checking. Afirst threshold (block 162) is specified for normal and clean tracks,and a second threshold is specified for noisy tracks or tracks in thebackground. The second threshold (block 164) is used if either the seedtrack or the current track is unreliable (e.g. at the boundaries, oreither track is noisy, or trkranges of both tracks are less than thespecified noisy track length threshold and at least one track is in thebackground) (block 160), otherwise the first threshold is used. If theshortest distance between the two tracks during their overlapping timeis less than the threshold (block 166), mark the current track as thebest fit track for the seed track (block 172) and if the seed track doesnot have best fit track yet or the current track is closer to the seedtrack than the existing best fit track (block 170). Go to step 3.

2.2.11 Merging of Tracks

This operation merges two tracks into one track and assigns the mergedtrack with properties derived from the two tracks. Most properties ofthe merged track are the sum of the corresponding properties of the twotracks but with the following exceptions:

Referring to FIG. 11, trk_enters and trk_exits properties of the mergedtrack are the sum of the corresponding properties of the tracks plus thecounts caused by zone crossing from the end point ozone track to thestart point of another track, which compensates the missing zonecrossing in the time gap between the two tracks (block 350).

If a point in time has multiple positions after the merge, the finalposition is the average (block 352).

The trk_start property of the merged track has the same trk_start valueas the newer track among the two tracks being merged, and the trk_endproperty of the merged track has the same trk_end value as the oldertrack among the two (block 354).

The buffered raw heights and raw positions of the merged track are thebuffered raw heights and raw positions of the older track among the twotracks being merged (block 356).

As shown in FIG. 13, an alternative embodiment of the present inventionmay be employed and may comprise a system 210 having an image capturingdevice 220, a reader device 225 and a counting system 230. In theillustrated embodiment, the at least one image capturing device 220 maybe mounted above an entrance or entrances 221 to a facility 223 forcapturing images from the entrance or entrances 221. The area capturedby the image capturing device 220 is field of view 244. Each imagecaptured by the image capturing device 220, along with the time when theimage is captured, is a frame 248. As described above with respect toimage capturing device 20 for the previous embodiment of the presentinvention, the image capturing device 220 may be video based. The mannerin which object data is captured is not meant to be limiting so long asthe image capturing device 220 has the ability to track objects in timeacross a field of view 244. The object data 261 may include manydifferent types of information, but for purposes of this embodiment ofthe present invention, it includes information indicative of a startingframe, an ending frame, and direction.

For exemplary purposes, the image capturing device 220 may include atleast one stereo camera with two or more video sensors 246 (similar tothe image capturing device shown in FIG. 2), which allows the camera tosimulate human binocular vision. A pair of stereo images comprisesframes 248 taken by each video sensor 246 of the camera. The imagecapturing device 220 converts light images to digital signals throughwhich the device 220 obtains digital raw frames 248 comprising pixels.The types of image capturing devices 220 and video sensors 246 shouldnot be considered limiting, and any image capturing device 220 and videosensor 246 compatible with the present system may be adopted.

For capturing tag data 226 associated with RFID tags, such as name tagsthat may be worn by an employee or product tags that could be attachedto pallets of products, the reader device 225 may employ active RFIDtags 227 that transmit their tag information at a fixed time interval.The time interval for the present invention will typically be between 1and 10 times per second, but it should be obvious that other timeintervals may be used as well. In addition, the techniques fortransmitting and receiving RFID signals are well known by those withskill in the art, and various methods may be employed in the presentinvention without departing from the teachings herein. An active RFIDtag is one that is self-powered, i.e., not powered by the RF energybeing transmitted by the reader. To ensure that all RFID tags 227 arecaptured, the reader device 225 may run continuously and independentlyof the other devices and systems that form the system 210. It should beevident that the reader device 225 may be replaced by a device that usesother types of RFID tags or similar technology to identify objects, suchas passive RFID, ultrasonic, or infrared technology. It is significant,however, that the reader device 225 has the ability to detect RFID tags,or other similar devices, in time across a field of view 228 for thereader device 225. The area captured by the reader device 225 is thefield of view 228 and it is preferred that the field of view 228 for thereader device 225 be entirely within the field of view 244 for the imagecapturing device 220.

The counting system 230 processes digital raw frames 248, detects andfollows objects 258, and generates tracks associated with objects 258 ina similar manner as the counting system 30 described above. The countingsystem 230 may be electronically or wirelessly connected to at least oneimage capturing device 220 and at least one reader device 225 via alocal area or wide area network. Although the counting system 230 in thepresent invention is located remotely as part of a central server, itshould be evident to those with skill in the art that all or part of thecounting system 230 may be (i) formed as part of the image capturingdevice 220 or the reader device 225, (ii) stored on a “cloud computing”network, or (iii) stored remotely from the image capturing device 220and reader device 225 by employing other distributed processingtechniques. In addition, the RFID reader 225, the image capturing device220, and the counting system 230 may all be integrated in a singledevice. This unitary device may be installed anywhere above the entranceor entrances to a facility 223. It should be understood, however, thatthe hardware and methodology that is used for detecting and trackingobjects is not limited with respect to this embodiment of the presentinvention. Rather, it is only important that objects are detected andtracked and the data associated with objects 258 and tracks is used incombination with tag data 226 from the reader device 225 to separatelycount and track anonymous objects 320 and defined objects 322, which areassociated with an RFID tag 227.

To transmit tag data 226 from the reader device 225 to a counting system230, the reader device 225 may be connected directly to the countingsystem 230 or the reader device 225 may be connected remotely via awireless or wired communications network, as are generally known in theindustry. It is also possible that the reader device 225 may send tagdata to the image capturing device, which in turn transmits the tag data226 to the counting system 230. The tag data 226 may be comprised ofvarious information, but for purposes of the present invention, the tagdata 226 includes identifier information, signal strength informationand battery strength information.

To allow the counting system 230 to process traffic data 260, tag data226 and object data 261 may be putted from the reader device 225 and theimage capturing device 220 and transmitted to the counting system 230.It is also possible for the reader device 225 and the image capturingdevice 220 to push the tag data 226 and object data 261, respectively,to the counting system 230. It should be obvious that the traffic data260, which consists of both tag data 226 and object data 261, may alsobe transmitted to the counting system via other means without departingfrom the teachings of this invention. The traffic data 260 may be sentas a combination of both tag data 226 and object data 261 and thetraffic data 260 may be organized based on time.

The counting system 230 separates the traffic data 260 into tag data 226and object data 261. To further process the traffic data 260, thecounting system 230 includes a listener module 310 that converts the tagdata 226 into sequence records 312 and the object data 261 into trackrecords 314. Moreover, the counting system 230 creates a sequence array352 comprised of all of the sequence records 312 and a track array 354comprised of all of the track records 314. Each sequence record 312 mayconsist of (1) a tag ID 312 a, which may be an unsigned integerassociated with a physical RFID tag 227 located within the field of view228 of a reader device 220; (2) a startTime 312 b, which may consist ofinformation indicative of a time when the RFID tag 227 was firstdetected within the field of view 228; (3) an endTime, which may consistof information indicative of a time when the RFID tag 227 was lastdetected within the field of view 228 of the reader device 220; and (4)an array of references to all tracks that overlap a particular sequencerecord 312. Each track record 314 may include (a) a counter, which maybe a unique ID representative of an image capturing device 220associated with the respective track; (b) a direction, which may consistof information that is representative of the direction of movement forthe respective track; (c) startTime, which may consist of informationindicative of a time when the object of interest was first detectedwithin the field of view 244 of the image capturing device 220; (d)endTime, which may consist of information indicative of a time when theobject of interest left the field of view 244 of the image capturingdevice 220; and (e) tagID, which (if non-zero) may include an unsignedinteger identifying a tag 227 associated with this track record 314.

To separate and track anonymous objects 320, such as shoppers orcustomers, and defined objects 322, such as employees and products, thecounting system 220 for the system must determine which track records314 and sequence records 312 match one another and then the countingsystem 220 may subtract the matching track records 312 fromconsideration, which means that the remaining (unmatched) track records314 relate to anonymous objects 320 and the track records 312 that matchsequence records 314 relate to defined objects 322.

To match track records 314 and sequence records 312, the counting system220 first determines which track records 314 overlap with particularsequence records 312. Then the counting system 220 creates an arraycomprised of track records 312 and sequence records 314 that overlap,which is known as a match record 316. In the final step, the countingsystem 220 iterates over the records 312, 314 in the match record 316and determines which sequence records 312 and track records 314 bestmatch one another. Based on the best match determination, the respectivematching track record 314 and sequence record 312 may be removed fromthe match record 316 and the counting system will then iteratively moveto the next sequence record 312 to find the best match for that sequencerecord 312 until all of the sequence records 312 and track records 314in the match record 316 have matches, or it is determined that no matchexists.

The steps for determining which sequence records 312 and track records314 overlap are shown in FIG. 15. To determine which records 312, 314overlap, the counting system 220 iterates over each sequence record 312in the sequence array 352 to find which track records overlap with aparticular sequence records 312; the term “overlap” generally refers totrack records 314 that have startTimes that are within a window definedby the startTime and endTime of a particular sequence records 312.Therefore, for each sequence record 312, the counting system 230 alsoiterates over each track record 314 in the track array 354 and adds areference to the respective sequence record 312 indicative of each trackrecord 314 that overlaps that sequence record 314. Initially, thesequence records have null values for overlapping track records 314 andthe track records have tagID fields set to zero, but these values areupdated as overlapping records 312, 314 are found. The iteration overthe track array 254 stops when a track record 314 is reached that has astartTime for the track record 314 that exceeds the endTime of thesequence record 312 at issue.

To create an array of “overlapped” records 312, 314 known as matchrecords 316, the counting system 230 iterates over the sequence array352 and for each sequence record 312 a, the counting system 230 comparesthe track records 314 a that overlap with that sequence record 312 a tothe track records 314 b that overlap with the next sequence record 312 bin the sequence array 352. As shown in FIG. 16, a match record 316 isthen created for each group of sequence records 312 whose track records314 overlap. Each match record 316 is an array of references to allsequence records 312 whose associated track records 314 overlap witheach other and the sequence records 312 are arranged inearliest-to-latest startTime order.

The final step in matching sequence records 312 and track records 314includes the step of determining which sequence records 312 and trackrecords 314 are the best match. To optimally match records 312, 314, thecounting system 230 must consider direction history on a per tag 227basis, i.e., by mapping between the tagID and the next expected matchdirection. The initial history at the start of a day (or work shift) isconfigurable to either “in” or “out”, which corresponds to employeesinitially putting on their badges or name tags outside or inside themonitored area.

To optimally match records 312, 314, a two level map data structure,referred to as a scoreboard 360, may be built. The scoreboard 360 hasatop level or sequencemap 362 and a bottom level or trackmap 364. Eachlevel 362, 364 has keys 370, 372 and values 374, 376. The keys 370 forthe top level 362 are references to the sequence array 352 and thevalues 374 are the maps for the bottom level 364. The keys for thebottom level 364 are references to the track array 354 and the values376 are match quality scores 380. As exemplified in FIG. 17, the matchquality scores are determined by using the following algorithm.

1) Determine if the expected direction for the sequence record is thesame as the expected direction for the track record. If they are thesame, the MULTIPLIER is set to 10. Otherwise, the MULTIPLIER is set to1.

2) Calculate the percent of overlap between the sequence record 312 andthe track record 314 as an integer between 0 and 100 by using theformula:OVERLAP=(earliest endTime−latest startTime)/(latest endTime−earlieststartTime)

If OVERLAP is <0, then set the OVERLAP to 0.

3) Calculate the match quality score by using the following formula:SCORE=OVERLAP×MULTIPLIER

The counting system 230 populates the scoreboard 360 by iterating overthe sequence records 312 that populate the sequence array 352 referencedby the top level 372 and for each of the sequence records 312, thecounting system 230 also iterates over the track records 314 thatpopulate the track array 354 referenced by the bottom level 374 andgenerates match quality scores 380 for each of the track records 314. Asexemplified in FIG. 18A, once match quality scores 380 are generated andinserted as values 376 in the bottom level 364, each match quality score380 for each track record 314 is compared to a bestScore value and ifthe match quality score 380 is greater than the bestScore value, thebestScore value is updated to reflect the higher match quality score380. The bestTrack reference is also updated to reflect the track record314 associated with the higher bestScore value.

As shown in FIG. 18B, once the bestTrack for the first sequence in thematch record is determined, the counting system 230 iterates over thekeys 370 for the top level 372 to determine the bestSequence, whichreflects the sequence record 312 that holds the best match for thebestTrack, i.e., the sequence record/track record combination with thehighest match quality score 380. The bestScore and bestSequence valuesare updated to reflect this determination. When the bestTrack andbestSequence values have been generated, the sequence record 312associated with the bestSequence is deleted from the scoreboard 360 andthe bestTrack value is set to 0 in all remaining keys 372 for the bottomlevel 364. The counting system 230 continues to evaluate the remainingsequence records 312 and track records 314 that make up the top andbottom levels 362, 364 of the scoreboard 360 until all sequence records312 and track records 314 that populate the match record 316 have beenmatched and removed from the scoreboard 360, or until all remainingsequence records 312 have match quality scores 380 that are less than orequal to 0, i.e., no matches remain to be found. As shown in Table 1,the information related to the matching sequence records 312 and trackrecords 314 may be used to prepare reports that allow employers totrack, among other things, (i) how many times an employee enters orexits an access point; (ii) how many times an employee enters or exitsan access point with a customer or anonymous object 320; (iii) thelength of time that an employee or defined object 322 spends outside;and (iv) how many times a customer enters or exits an access point. Thisinformation may also be used to determine conversion rates and other“What If” metrics that relate to the amount of interaction employeeshave with customers. For example, as shown in Table 2, the system 210defined herein may allow employers to calculate, among other things: (a)fitting room capture rates; (b) entrance conversion rates; (c) employeeto fitting room traffic ratios; and (d) the average dollar spent. Thesemetrics may also be extrapolated to forecast percentage sales changesthat may result from increases to the fitting room capture rate, asshown in Table 3.

In some cases, there may be more than one counter 222, which consists ofthe combination of both the image capturing device 220 and the readerdevice 225, to cover multiple access points. In this case, separatesequence arrays 352 and track arrays 354 will be generated for each ofthe counters 222. In addition, a match array 318 may be generated andmay comprise each of the match records 316 associated with each of thecounters 222. In order to make optimal matches, tag history must beshared between all counters 222. This may be handled by merging, in atime-sorted order, all of the match records in the match array 318 andby using a single history map structure, which is generally understoodby those with skill in the art. When matches are made within the matcharray 318, the match is reflected in the track array 354 associated witha specific counter 222 using the sequence array 352 associated with thesame counter 222. This may be achieved in part by using a counter IDfield as part of the track records 314 that make up the track array 354referenced by the bottom level 364 of the scoreboard 360. For example,references to the track arrays 354 may be added to a total track array356 and indexed by counter ID. The sequence arrays 352 would be handledthe same way.

As shown in FIG. 19, a further embodiment of the present invention maybe employed and may comprise a system 1210 having one or more sensors1220, a data capturing device 1225 and a counting system 1230. In theillustrated embodiment, the sensor(s) 1220 may be image capturingdevices and may be mounted above an entrance or entrances 1221 to afacility 1223 for capturing images from the entrance or entrances 1221and object data 1261. The area captured by the sensor 1220 is a field ofview 1244. Each image captured by the sensor 1220, along with the timewhen the image is captured, is a frame 1248. As described above withrespect to image capturing device 20 in connection with a separateembodiment of the present invention, the sensor 1220 may be video based.The object data 1261 may include many different types of information,but for purposes of this embodiment of the present invention, itincludes information indicative of a starting frame, an ending frame,and direction. It should be understood that the sensor 1220 may alsoemploy other technology, which is widely known the industry. Therefore,the manner in which object data 1261 is captured is not meant to belimiting so long as the sensor 1220 has the ability to track objects intime across a field of view 1244.

For exemplary purposes, the sensor 1220 may include at least one stereocamera with or more video sensors 1246 (similar to the sensor shown inFIG. 2), which allows the camera to simulate human binocular vision. Apair of stereo images comprises frames 1248 taken by each video sensor1246 of the camera. The sensor 1220 converts light images to digitalsignals through which the counting system 1330 obtains digital rawframes 1248 comprising pixels. Again, the types of sensors 1220 andvideo sensors 1246 should not be considered limiting, and it should beobvious that any sensor 1220, including image capturing devices, thermalsensors and infrared video devices, capable of counting the total foottraffic and generating a starting frame, an ending frame, and adirection will be compatible with the present system and may be adopted.

For providing more robust tracking and counting information, the objectdata 1261 from the sensor 1220 may be combined with subset data 1226that is captured by the data capturing device 1225. The subset data 1226may include a unique identifier 1232A, an entry time 1232B, an exit time1232C and location data 1262 for each object of interest 1258. Thesubset data 1226 may be generated by data capturing devices 1225 thatutilize various methods that employ doorway counting technologies,tracking technologies and data association systems. Doorway countingtechnologies, such as Bluetooth and acoustic based systems, are similarto the RFID system described above and generate subset data 1226. Togenerate the subset data 1226, the doorway counting technology mayprovide a control group with a device capable of emitting a particularsignal (i.e., Bluetooth or sound frequency). The system may then monitora coverage area 1228 and count the signals emitted by the devicesassociated with each member of the control group to generate subset data1226. The subset data 1226 from the doorway counting system may becombined with the object data 1261 from the sensor 1220 to generatecounts related to anonymous objects and defined objects. The doorwaycounting system may also be video based and may recognize faces, gender,racial backgrounds or other immutable characteristics that are readilyapparent.

The data capturing device 1225 may also use tracking technology thattriangulates on a cellular signals emitted from a mobile handsets 1227to generate location data 1262. The cellular signal may be T-IMSI(associated with GSM systems), CDMA (which is owned by the CDMADevelopment Group), or Wi-Fi (which is owned by the Wi-Fi Alliance)signals. The data capturing device 1225 may also receive location data1262, such as GPS coordinates, for objects of interest from a mobilehandset 1227. The location data 1262 may be provided by a mobileapplication on the mobile handset or by the carrier for the mobilehandset 1227. User authorization may be required before mobileapplications or carriers are allowed to provide location data 1262.

For ease of reference, we will assume that the subset data 1226discussed below is based on the location data 1262 that is provided by amobile handset 1227. The data capturing device 1225 receives subset data1226 associated with a mobile handset 1227, which is transmitted at afixed time interval. The time interval for the present invention willtypically be between 1 and 10 times per second, but it should be obviousthat other time intervals may be used as well. In addition, thetechniques for transmitting and receiving the signals from mobilehandsets are well known by those with skill in the art, and variousmethods may be employed in the present invention without departing fromthe teachings herein. To ensure that the subset data 1226 for all mobilehandsets 1227 is captured, the data capturing device 1225 may runcontinuously and independently of the other devices and systems thatform the system 1210. As mentioned above, the data capturing device 1225may employ various types of signals without departing from the scope ofthis application. It is significant, however, that the data capturingdevice 1225 has the ability to track mobile handsets 1227, or othersimilar devices, in time across a coverage area 1228. The area capturedby the data capturing device 1225 is the coverage area 1228. In someinstances, the field of view 1244 for the sensor may be entirely withinthe coverage area 1228 for the data capturing device 1225 and viceversa.

The data capturing device 1225 may also use data association systems1245. Data association systems 1245 take data from other independentsystems such as point of sale systems, loyalty rewards programs, pointof sale trigger information (i.e., displays that attach cables tomerchandise and count the number of pulls for the merchandise),mechanical turks (which utilize manual input of data), or other similarmeans. Data generated by data association systems 1245 may not includeinformation related to direction, but it may include more detailedinformation about the physical characteristics of the object ofinterest.

The counting system 1230 may process digital raw frames 1248, detect andfollow objects of interest (“objects”) 1258, and may generate tracksassociated with objects 1258 in a similar manner as the counting system30 described above. The counting system 1230 may be electronically orwirelessly connected to at least one sensor 1220 and at least one datacapturing device 1225 via a local area or wide area network. Althoughthe counting system 1230 in the present invention is located remotely aspart of a central server, it should be evident to those with skill inthe art that all or part of the counting system 1230 may be (i) formedas part of the sensor 1220 or the data capturing device 1225, (ii)stored on a “cloud computing” network, or (iii) stored remotely from thesensor 1220 and data capturing device 1225 by employing otherdistributed processing techniques. In addition, the data capturingdevice 1225, the sensor 1220, and the counting system 1230 may all beintegrated in a single device. This unitary device may be installedanywhere above the entrance or entrances to a facility 1223. It shouldbe understood, however, that the hardware and methodology that is usedfor detecting and tracking objects is not limited with respect to thisembodiment of the present invention. Rather, it is only important thatobjects are detected and tracked and the data associated with objects1258 and tracks is used in combination with subset data 1226 from thedata capturing device 1225 to separately count and track anonymousobjects 1320 and defined objects 1322, which are associated with themobile handset 1227.

To transmit subset data 1226 from the data capturing device 1225 to acounting system 1230, the data capturing device 1225 may be connecteddirectly to the counting system 1230 or the data capturing device 1225may be connected remotely via a wireless or wired communicationsnetwork, as are generally known in the industry. It is also possiblethat the data capturing device 1225 may send subset data 1226 to thesensor 1220, which in turn transmits the subset data 1226 to thecounting system 1230. The subset data 1226 may be comprised of variousinformation, but for purposes of the present invention, the subset data1226 includes a unique identifier, location based information and one ormore timestamps.

To allow the counting system 1230 to process traffic data 1260, subsetdata 1226 and object data 1261 may be pulled from the data capturingdevice 1225 and the sensor 1220, respectively, and transmitted to thecounting system 1230. It is also possible for the data capturing device1225 and the sensor 1220 to push the subset data 1226 and object data1261, respectively, to the counting system 1230. It should be obviousthat the traffic data 1260, which consists of both subset data 1226 andobject data 1261, may also be transmitted to the counting system viaother means without departing from the teachings of this invention. Thetraffic data 1260 may be sent as a combination of both subset data 1226and object data 1261 and the traffic data 1260 may be organized based ontime.

The counting system 1230 separates the traffic data 1260 into subsetdata 1226 and object data 1261. To further process the traffic data1260, the counting system 1230 may include a listener module 1310 thatconverts the subset data 1226 into sequence records 1312 and the objectdata 1261 into track records 1314. Moreover, the counting system 1230may create a sequence array 1352 comprised of all of the sequencerecords 1312 and a track array 1354 comprised of all of the trackrecords 1314. Each sequence record 1312 may consist of (1) a unique ID1312 a, which may be an unsigned integer associated with a mobilehandset 1227, the telephone number associated with the mobile handset1227 or any other unique number, character, or combination thereofassociated with the mobile handset 1227; (2) a startTime 1312 b, whichmay consist of information indicative of a time when the mobile handset1227 was first detected within the coverage area 1228; (3) an endTime,which may consist of information indicative of a time when the mobilehandset 1227 was last detected within the coverage area 1228 of the datacapturing device 1225; and (4) an array of references to all tracks thatoverlap a particular sequence record 1312. Each track record 1314 mayinclude (a) a counter, which may be a unique ID representative of asensor 1220 associated with the respective track; (b) a direction, whichmay consist of information that is representative of the direction ofmovement for the respective track; (c) startTime, which may consist ofinformation indicative of a time when the object of interest was firstdetected within the field of view 1244 of the sensor 1220; (d) endTime,which may consist of information indicative of a time when the object ofinterest left the field of view 1244 of the sensor 1220; and (e)handsetID, which (if non-zero) may include an unsigned integeridentifying a mobile handset 1227 associated with this track record1314.

To separate and track anonymous objects 1320 (i.e., shoppers or randomcustomers) and defined objects 1322 (such as shoppers with identifiedmobile handsets 1227 or shoppers with membership cards, employee badges,rail/air tickets, rental car or hotel keys, store-sponsored credit/debitcards, or loyalty reward cards, all of which may include RFID chips),the counting system 1230 for the system must determine which trackrecords 1314 and sequence records 1312 match one another and then thecounting system 1230 may subtract the matching track records 1314 fromconsideration, which means that the remaining (unmatched) track records1314 relate to anonymous objects 1320 and the track records 1314 thatmatch sequence records 1312 relate to defined objects 1322. The stepsrelated to matching track records 1314 and sequence records 1312 aredescribed in detail above. A similar method for matching track records1314 and sequence records 1312 may be employed in connection with thepresent embodiment of the counting system 1230. The match algorithm orquality score algorithm that is employed should not be viewed aslimiting, as various methods for matching tracks and sequence records1314, 1312 may be used.

In some cases, there may be more than one counting system 1230, whichconsists of the combination of both the sensor 1220 and the datacapturing device 1225, to cover multiple access points. In this case,separate sequence arrays 1352 and track arrays 1354 will be generatedfor each of the counters 1222. In addition, a match array 1318 may begenerated and may comprise each of the match records 1316 associatedwith each of the counting systems 1230. In order to make optimalmatches, tag history must be shared between all counting systems 1230.This may be handled by merging, in a time-sorted order, all of the matchrecords in the match array 1318 and by using a single history mapstructure, which is generally understood by those with skill in the art.

By identifying, tracking and counting objects 1258 simultaneously withone or more sensors 1220 and one or more data capturing devices 1225,the system 1210 can generate data sets based on time, geography,demographic characteristics, and behavior. For example, the system 1210may be capable of determining or calculating the following:

-   -   a. The actual length of time the overall population or subsets        of the population dwell at specified locations;    -   b. The actual time of entry and exit thr the overall population        and subsets of the population;    -   c. The overall density of the overall population and subsets of        the population at specified points in time;    -   d. Flow time of day impacts the behaviors of the overall        population and subsets of the population;    -   e. The actual location and paths for subsets of the population;    -   f. The actual entry and exit points for subsets of the        population;    -   g. The density of subsets of the population;    -   h. Whether the geography or location of departments or products        impacts the behaviors of subsets of the population;    -   i. The actual demographic and personal qualities associated with        the subset of the population;    -   j. How demographic groups behave in particular geographic        locations, at specified times or in response to trigger events,        such as marketing campaigns, discount offers, video, audio or        sensory stimulation, etc.;    -   k. The overall density of various demographic groups based on        time, geography or in response to trigger events;    -   l. How the behavior of demographic groups changes based on time,        geography or in response to trigger events;    -   m. Any data that may be generated in relation to subsets of the        population may also be used to estimate similar data for the        overall population;    -   n. Data captured for specific areas, zones or departments may be        dependent upon one another or correlated; and    -   o. Combinations of the foregoing data or reports.

Although there are a multitude of different data types and reports thatmay be calculated or generated, the data generally falls into thefollowing four dimensions 1380: time 1380A, geographic 1380B,demographic 1380C and behavioral 1380D. These various dimensions 1380 orcategories should not, however, be viewed as limiting as it should beobvious to those with skill in the art that other dimensions 1380 ordata types may also exist.

To generate data related to the time dimension 1380A, various algorithmsmay be applied. For example, the algorithm in FIG. 20 shows a series ofsteps that may be used to calculate dwell time 1400 for one or moreshoppers or to count the frequent shopper data or number of visits 1422by one or more shoppers. As shown at step 2110, to calculate dwell time1400 or frequent shopper data 1420, the system 1210 must first uploadthe traffic data 1260, including the object data 1261 and the subsetdata 1226. The traffic data 1260 may also include a unique ID 1312A, astart time 1312B and an end time 1312C for each shopper included as partof the traffic data 1260. The start times 1312B and end times 1312C maybe associated with specific predefined areas 1402. To generate reportsfor specific time periods 1313, the system 1210 may also sort thetraffic data 1260 based on a selected time range. For example, at step2120 the system 1210 may aggregate the traffic data 1260 based on thestart time 1312B, the end time 1312C or the time period 1313.Aggregating traffic data 1260 based on start time 1312B, end time 1312Cor time period 1313 is generally understood in the industry and theorder or specific steps that are used for aggregating the traffic data1260 should not be viewed as limiting. Once the traffic data 1260 isaggregated or sorted, it may be used to calculate dwell times 1400 orfrequent shopper data 1420. It should be understood that the timeperiods 1313 may be based on user-defined periods, such as minutes,hours, days, weeks, etc.

To allow a user to generate either dwell time 1400 or frequent shopperdata 1420, the system 1210 may ask the user to select either dwell time1400 or frequent shopper data 1420 (see step 1230). This selection andother selections that are discussed throughout this description may beeffectuated by allowing a user to select a button, a hyperlink or anoption listed on a pull-down or pop-up menu, or to type in the desiredoption in a standard text box. Other means for selecting reportingoptions may also be employed without departing from the presentinvention. To calculate the dwell time 1400 for a shopper, the followingequation may be used:Dwell time=(Time at which a shopper leaves a predefined area)−(Time atwhich a shopper enters a predefined area)

For example, assume a shopper enters a predefined area 1402, such as astore, at 1:00 pm and leaves at 1:43 pm. The dwell time 1400 would becalculated as follows:Dwell time=1:43−1:00=43 minutesIn this instance, the dwell time 1400 would be the total amount of time(43 minutes) that was spent in the predefined area 1402. The predefinedarea 1402 may be defined as a specific department, i.e., the men'sclothing department, the women's clothing department, a general area, aparticular display area, a point of purchase, a dressing room, etc.Thus, if a shopper visits multiple predefined areas 1402 or departments,the system 1210 may calculate dwell times for each predefined area 1402or department that is visited.

Predefined areas 1402 may be determined by entering a set of boundaries1404 obtained from a diagram 1406 of the desired space or store. The setof boundaries 1404 may define particular departments, display areaswithin particular departments or other smaller areas as desired. Morespecifically, the predefined areas 1402 may be determined by copying thediagram 1406 into a Cartesian plane, which uses a coordinate system toassign X and Y coordinates 1408 to each point that forms a set ofboundaries 1404 associated with the predefined areas 1402. Track records1314 may be generated for each shopper and may include an enter time1410 and exit time 1412 for each predefined area 1402. In order tocalculate dwell times 1400, the following information may be necessary:(i) unique Ds 1312A for each shopper; (ii) a set of boundaries 1404 foreach predefined area 1402; (iii) coordinates 1408 for set of boundaries1404 within a predefined areas 1402; (iv) enter times 1410 and exittimes 1412 for each predefined area 1402 and shopper. In addition toaggregating the traffic data 1260 and prior to calculating the dwelltimes 1400, the system 1210 may also sort the traffic data 1260 by timeperiods 1313, unique IDs 1312A, and/or the predefined areas 1402 visitedwithin the time periods 1313. The process of calculating dwell times1400 is an iterative and ongoing process, therefore, the system 1210 maystore dwell times 1400 by shopper or predefined area 1402 for later useor use in connection with other dimensions 1380. Dwell times 1400 may beused to generate reports that list aggregate or average dwell times 1400for selected shoppers, geographies, time periods or demographic groupsand these reports may also be feed into other systems that may use thedwell time 1400 to function, such as HVAC systems that may raise orlower temperatures based on the average dwell times 1400 for varioustime periods and geographic locations. It should be obvious to thosewith skill in the art that the dwell times 1400 and the dwell timereports 1400A may be used in many different ways. Therefore, the priordisclosure should be viewed as describing exemplary uses only and shouldnot limit the scope potential uses or formats for the dwell times 1400or dwell time reports 1400A.

To generate frequent shopper data 1420, the system 1210 may first uploadshopper visit data 1450 for a particular time period 1313. Shopper visitdata 1450 may be generated from the dwell times 1400 for a predefinedarea 1402 without regard to the length of the time associated with thedwell times 1400. In other words, step 2134 may increment a counter 1454for each shopper that enters a predefined area 1402. Then the system1210 may populate the shopper visit data 1450 with the data from thecounter 1454 and store the shopper visit data 1450 in a database 1452.The database 1452 may sort the shopper visit data 1450 based on a uniqueID 1312A for a particular shopper, a predefined area 1402, or a desiredtime period 1313. To avoid including walk-through traffic as part of theshopper visit data 1450, the system 1210 may require a minimum dwelltime 1400 in order to register as a visit and thereby increment thecounter 1454. The minimum dwell time 1400 may be on the order ofseconds, minutes, or even longer periods of time. Similar to dwell times1400, the shopper visit data 1450 may be sorted based on time periods1313, unique IDs 1312A, and/or the predefined areas 1402. For example,as shown in step 2136, the shopper visit data 1450 may be used togenerate a shopper frequency report 1450A by geography or based on apredefined area 1402. More specifically, the shopper frequency report1450A may show information such as the percentage of repeat shoppersversus one-time shoppers for a given time period 1313 or thedistribution of repeat shoppers by predefined areas 1402. The shopperfrequency report 1450A may also disclose this information based onspecified demographic groups. It should be obvious to those with skillin the art that the shopper visit data 1450 and the shopper frequencyreports 1450A may be used in many different ways. Therefore, the priordisclosure should be viewed as describing exemplary uses only and shouldnot limit the scope potential uses or formats for the shopper visit data1450 or shopper frequency reports 1450A.

To generate data related to the geographic dimension 1380B, severaldifferent methods may be employed. The algorithm shown in FIG. 21 showsone example of a series of steps that may be used to analyze foottraffic by geography. After it is determined in step 2200 that thegeographic analysis should be conducted, the system 1210 must firstupload the traffic data 1260, including the object data 1261, and thesubset data 1226. The subset data 1226 includes a unique identifier1232A, an entry time 1232B, an exit time 1232C and location data 1262for each object of interest 1258. The location data 1262 for object ofinterests 1258 is particularly important for generating data related tothe geographic dimension 1380B. As mentioned above, the location data1262 may be generated by using information from fixed receivers and arelative position for a shopper to triangulate the exact position of ashopper within a predefined area 1402 or by receiving GPS coordinatesfrom a mobile handset. The exact position of a shopper within apredefined area 1402 is determined on an iterative basis throughout theshopper's visit. Other methods for calculating exact position of ashopper may also be used without departing from the teachings providedherein.

To determine the position of an object of interest 1258 within apredefined area 1402, the location data 1262 may be associated with thepredefined area 1402. As mentioned above, the predefined area 1402 maybe defined by entering a set of boundaries 1404 within a diagram 1406 ofthe desired space or store. The diagram 1406 of the desired space orstore may be generated by the system 1210 from an existing map of thedesired space or store. Again, as referenced above, boundaries 1404 maybe generated by copying the diagram 1406 into a Cartesian plane andassigning X and Y coordinates 1408 to each point that forms an externalpoint on one or more polygons associated with the predefined area 1402.By iteratively tracking the position of an object of interest 1258 overtime, the system 1210 may also generate the direction 1274 in which theobject of interest 1258 is traveling and path data 1272 associated withthe path 1271 traveled by the object of interest 1258. The position ofan object of interest 1258 within a predefined area 1402 may also begenerated by: (1) using fixed proximity sensors that detect nearbyobjects of interest 1258, (2) triangulation of multiple referencesignals to produce the position of an object of interest 1258, or (3)using cameras that track video, infrared or thermal images produced byan object of interest 1258. Other means for generating the position ofan object of interest 1258 may also be used without departing from theteachings herein.

To generate path data 1272 for an object of interest 1258, the system1210 may use subset data 1226, including location data 1262, associatedwith an object of interest 1258 to iteratively plot X and Y coordinates1408 for an object of interest 1258 within a predefined area 1402 of adiagram 1406 at sequential time periods. Therefore, the X and Ycoordinates 1408 associated with an object of interest 1258 are alsolinked to the time at which the X and Y coordinates 1408 were generated.To count and track objects of interest 1258 in predefined areas 1402that are comprised of multiple floors, subset data 1226 and/or locationdata 1262 may also include a Z coordinate 1408 associated with the flooron which the object of interest 1258 is located. A multi-floor diagramis shown in FIG. 22.

As shown in step 2220, after location data 1262 and path data 1272 aregenerated for objects of interest 1258, that information should bestored and associated with the respective objects of interest 1258. Asshown in step 2230, the system 1210 allows a user to analyze trafficdata 1260 based on a predefined area 1402 or by path data 1272 forobjects of interest 1258. The predefined area 1402 may be a particulargeographic area, a mall, a store, a department within a store, or othersmaller areas as desired. To analyze the traffic, the system 1210 mayfirst aggregate the traffic data 1260 for the objects of interest 1258within the predefined area 1402 (see step 2232). As shown in step 2234,the system 1210 may subsequently load dwell times 1400, shopper visitdata 1450, sales transaction data 1460 or demographic data 1470 for theobjects of interest 1258. Step 2236 generates store level traffic data1480 for predefined areas 1402 by extrapolating the subset data 1226 forparticular predefined areas 1402 based on the corresponding object data1261, which may include the total number of objects of interest 1258within a predefined area 1402. By using the traffic data 1260, whichincludes object data 1261 and subset data 1226, and as shown in Step2238, the system 1210 may generate reports that show the number ofobjects of interest 1258 for a predefined area 1402, the number ofobjects of interest for a predefined area 1402 during a specific timeperiod 1313, the number of predefined areas 1402 that were visited (suchas shopper visit data 1420), or the dwell times 1400 for predefinedareas 1402. These reports may include information for specific subsetsof objects of interest 1258 or shoppers, or the reports may includestore level traffic data 1480, which extrapolates subset data 1226 to astore level basis. It should be obvious that the reports may alsoinclude other information related to time, geography, demographics orbehavior of the objects of interest 1258 and therefore, the foregoinglist of reports should not be viewed as limiting the scope of system1210.

At step 2230, the user may also choose to analyze traffic data 1260based on path data 1272 for objects of interest 1258 or the path 1271taken by objects of interest 1258. To analyze the traffic data 1260based on path data 1272 or the path 1271 taken, the system 1210 mayfirst load the location data 1262 and path data 1272 that was generatedfor objects of interest 1258 in step 2210. The system 1210 may also loadinformation, such as dwelt times 1400, shopper visit data 1450, salestransaction data 1460 or demographic data 1470 for the objects ofinterest 1258. Once this information is loaded into system 1210, thesystem 1210 may aggregate the most common paths 1271 taken by objects ofinterest 1258 and correlate path data 1272 information with dwell times1400 (see step 2244). After the information is aggregated andcorrelated, various reports may be generated at step 2246, includingreports that show (i) the most common paths that objects of interesttake in a store by planogram, including corresponding dwell times ifdesired, (ii) changes in shopping patterns by time period or season, and(iii) traffic patterns for use by store security or HVAC systems inincreasing or decreasing resources at particular times. These reportsmay be stored and used in connection with generating data and reportsfor other dimensions 1380. As shown in FIGS. 23 and 24, reports may alsobe generated based on the behavioral and demographic dimensions 1380C,1380D.

To generate data related to the demographic dimension 1380C, thealgorithm shown in FIG. 23 may be employed. As shown at step 2300 inFIG. 23, the system 1210 should first load traffic data 1260 anddemographic data 1500. The demographic data 1500 may be received from aretail program, including loyalty programs, trigger marketing programsor similar marketing programs, and may include information related to ashopper, including age, ethnicity, sex, physical characteristics, size,etc. The traffic data 1260 may be sorted in accordance with thedemographic data 1500 and other data, such as start time 1312B, end time1312C, time period 1313 and location data 1262. Once the traffic data1262 and demographic data 1500 is loaded and sorted, the system 1210 mayanalyze the traffic data 1260, including the object data 1261 and subsetdata 1226 based on various demographic factors, including, but notlimited to, shopper demographics, in-store behavior and marketingprograms.

To analyze the traffic data 1260 based on the shopper demographics (step2320), the system 1210 may sort the traffic data 1260 by geography andtime period. It should be obvious, however, that the traffic data 1260may also be sorted according to other constraints. In step 2322, thesystem 1210 analyzes the traffic data 1260, including object data 1261and subset data 1226) based on time periods 1313 and predefined areas1402. The analysis may include counts for objects of interest 1258 thatenter predefined areas 1402 or counts for objects of interest 1258 thatenter predefined areas 1402 during specified time periods 1313. Toassist in analyzing the traffic data 1260 based on time periods 1313 andpredefined areas 1402, the information that was generated in connectionwith the time dimension 1380A, including dwelt times 1400, shopper data1420 and shopper visit data 1450, and the geographic dimension 1380B,i.e., predefined areas 1402, boundaries 1404 and x and y coordinates,may be used. As shown in step 2324, the system 1210 may generate reportsthat show traffic by demographic data 1500 and store, department, timeof day, day of week, or season, etc.

To analyze traffic data 1260 based on in-store behavior, the system 1210must determine whether to look at in-store behavior based on dwell time1400 or path data 1272. If dwell time is selected (step 2336), thesystem 1210 may load dwelt times 1400 generated in relation to the timedimension 1380A and sort the dwell times 1400 based on the geographicdimension 1380B. After the dwell times 1400 and path data 1272 arecombined with the demographic data 1500, the system 1210 may generatereports that show the dwell times 1400 by profile characteristics ordemographic data 1500, i.e., age, ethnicity, sex, physicalcharacteristics, size, etc. For analyzing traffic data 1260 based on acombination of demographic data 1500 and path data 1272, the system 1210may first load path data 1272 generated in relation to the geographicdimension 1380B. After the path data 1272 is sorted according toselected demographic data 1500, the system 1210 may then generatereports that show the number of departments visited by objects ofinterest 1258 associated with particular demographic groups, the mostcommon paths used by the objects of interest 1258 associated with thosedemographic groups. It should be obvious that other reports associatedwith demographic groups and path data 1272 may also be generated by thesystem 1210.

To analyze the traffic data based on a particular marketing program, thesystem 1210 may first load demographic data 1500 associated with aparticular marketing program 1510. The marketing program 1510 may beaimed at a specific product or it may be a trigger marketing programbeing offered by a retailer. Examples of such programs are:“percent-off” price promotion for items in a specific department; e-mail“blasts” sent to customers promoting certain products; time-baseddiscounted upsell offerings made available to any female entering thestore; a “flash loyalty” discount for any customer making a return tripto the store within a 10-day period; upon entering the store sending ashopper a message informing her of a trunk show event taking place laterthat day; and provide shoppers with the option to download an unreleasedsong from a popular band made available as part of national ad campaign.Other types of marketing programs 1510 may also be the source of thedemographic data without departing from the teaching and tenets of thisdetailed description. To analyze the traffic data based on the marketingprogram 1510, the system 1210 may first sort the traffic data 1260 basedon the demographic data 1500 provided by the marketing programs. Asshown in step 2342, the system 1210 may then generate reports that showretailer loyalty program driven behavior demographics, such as theimpact of loyalty programs on particular demographic groups, the trafficof those demographic groups or shopping habits of those demographicgroups. These reports may include: information related to how specificdemographic groups (teens, young adults, seniors, males, females, etc.)shop in specific departments or areas of a store; traffic reports withinformation related to before and after a targeted promotion is offeredto “loyal” customers; benchmark information regarding the effects ofmarketing programs on traffic and sales within the store; andcomparisons of traffic by department for a targeted demographicpromotion.

To generate data related to the behavioral dimension 1380D, the system1210 may combine data and reports generated in connection with the otherdimensions 1380, namely, the time dimension 1380A, geographic dimension1380B and demographic dimension 1380C. In addition, conversion rateanalysis or purchaser/non-purchaser analysis may also be added to thedata and reports generated in connection with the other dimensions 1380.To generate conversion rates 1600, purchase data 1610, which includespurchaser 1610A and non-purchaser data 1610B, the algorithm shown inFIG. 24 may be employed. As shown at step 2400 in FIG. 24, the firststep is to determine whether to generate conversion rates 1600 orpurchase data 1610.

For generating conversion rates 1600, the system may first load trafficdata 1260 and transaction data 1620 from the geographic dimension 1380Band sort the traffic data 1260 by time periods 1313. The transactiondata 1620 may include information related to the sales amount, thenumber of items that were purchased, the specific items that werepurchased, the date and time of the transaction, the register used tocomplete the transaction, the location where the sale was completed(department ID and sub-department ID), and the sales associate thatcompleted the sale. Other information may also be gathered as part ofthe transaction data 1620 without departing from the teaching herein.Next, the system 1210 may load projected traffic data from thegeographic dimension 1380B, which is also sorted by time periods 1313.In step 2414, the system 1210 may calculate conversion rates 1600. Theconversion rates may be calculated by dividing the transactions by thetraffic counts within a predefined area 1402 by a time period 1313. Forinstance, for one hour of a day a department in a store generated twentysales (transactions). During the same hour, one hundred people visitedthe department. The department's conversion rate was twenty percent(20%) (20 transactions/100 shoppers). The conversion rates 1600 may alsobe associated with a demographic factor 1630. For instance, given thetype of store, males or females (gender demographic) may convert, atdifferent rates. The conversion rates 1600 that are calculated may bestored by the system 1210 for later use in step 2416. Once theconversion rates 1600 are calculated, they may be used to generatereports such as the conversion rate 1600 for an entire store or aspecific department, the conversion rate 1600 for shoppers with specificprofiles or characteristics, cross-sections of the above-referencedconversion rates 1600 based on a time period 1313, or other reportsbased on combinations of the conversion rate 1600 with information fromthe time dimension 1380A, geographic dimension 1380B or demographicdimension 1380C.

For generating reports based on purchaser behavior, the system 1210 mayfirst load dwell times 1400 and then sort the dwell times 1400 bypredefined areas 1402 (see step 2420). The dwell times 1400 may befurther sorted by time periods 1313. As shown in step 2422, the system1210 may then load transaction data 1620 (such as sales transactions)for the same predefined areas 1402 and corresponding time periods 1313.The system 1210 may then produce reports that show comparisons betweenpurchasers and non-purchasers based on dwell times 1400, predefinedareas 1402 and/or time periods 1313. These reports may also be sortedbased on demographic data 1470, as mentioned above.

The invention is not limited by the embodiments disclosed herein and itwill be appreciated that numerous modifications and embodiments may bedevised by those skilled in the art. Therefore, it is intended that thefollowing claims cover all such embodiments and modifications that fallwithin the true spirit and scope of the present invention.

REFERENCES

-   [1] C. Wren, A. Azarbayejani, T. Darrel and A. Pentland. Pfinder:    Real-time tracking of the human body. In IEEE Transactions on    Pattern Analysis and Machine Intelligence, July 1997, Vol 19, No. 7,    Page 780-785.-   [2] 1. Haritaoglu, D. Harwood and L. Davis. W4: Who? When? Where?    What? A real time system for detecting and tracking people.    Proceedings of the Third IEEE International Conference on Automatic    Face and Gesture Recognition, Nara, Japan, April 1998.-   [3] M. Isard and A. Blake, Contour tracking by stochastic    propagation of conditional density. Proc. ECCV 1996.-   [4] P. Remagnino, P. Brand and R. Mohr, Correlation techniques in    adaptive template matching with uncalibrated cameras. In Vision    Geometry III, SPIE Proceedings vol. 2356, Boston, Mass., 2-3 Nov.    1994-   [5] C. Eveland, K. Konolige, R. C. Bolles, Background modeling for    segmentation of video-rate stereo sequence. In Proceedings of the    IEEE Conference on Computer Vision and Pattern Recognition, page    226, 1998.-   [6] J. Krumm and S. Harris, System and process for identifying and    locating people or objects in scene by selectively slustering    three-dimensional region. U.S. Pat. No. 6,771,818 BI, August 2004.-   [7] T. Darrel, G. Gordon, M. Harville and J. Woodfill, Integrated    person tracking using stereo, color, and pattern detection. In    Proceedings of the IEEE Conference on Computer Vision and Pattern    Recognition, page 601609, Santa Barbara, June 1998.

What is claimed:
 1. A method for counting and tracking defined objectsand anonymous objects, comprising the steps of: capturing object datawith a sensor, wherein the object data is associated with both definedobjects and anonymous objects that pass an entrance; receiving subsetdata with a data capturing device, wherein the subset data is associatedwith defined objects and includes a unique identifier, an entry time, anexit time, and location data for each defined object; receiving objectdata and subset data at a counting system; identifying matching objectand subset data, wherein matches are representative of the definedobjects; counting the defined objects and the anonymous objects;tracking the defined objects and the anonymous objects; associating alocation of a defined object with a predefined area; generating pathdata by plotting X and Y coordinates for the defined object within thepredefined area at sequential time periods; and receiving data fromanother independent system regarding characteristics of the object. 2.The method of claim 1, wherein the object data includes at least astarting frame, an ending frame, and a direction.
 3. The method of claim1, wherein the other independent system is selected from the groupconsisting of point of sale systems, loyalty rewards systems, point ofsale trigger information, and mechanical turks.
 4. The method of claim1, further including the steps of converting the subset data intosequence records and converting the object data into track records. 5.The method of claim 4, further including the step of creating a sequencearray of all of the sequence records, wherein a sequence recordcomprises: (a) a unique ID, which is an unsigned integer associated witha mobile handset, a telephone number associated with the mobile handsetor any other unique number, character, or combination thereof,associated with object; (b) a start time, which may consist ofinformation indicative of a time when the object was first detectedwithin a coverage area; (c) an end time, which may consist ofinformation indicative of a time when the object was last detectedwithin the coverage area of the data capturing device; and (d) an arrayof references to all tracks that overlap a particular sequence record.6. The method of claim 5, further including the step of creating a trackarray of all of the track records, wherein a track record comprises: (a)a counter, which is a unique ID representative of a sensor associatedwith an object; (b) a direction, which may consist of information thatis representative of the direction of movement for the object; (c) astart time, which may consist of information indicative of a time whenthe object was first detected within a field of view of the sensor; (d)an end time, which may consist of information indicative of a time whenthe object left the field of view of the sensor; and (e) a handset ID,which identifies a mobile handset associated with a particular object.7. The method of claim 1, further including the step of using Zcoordinates to further track objects within a predefined area defined bymultiple floors.
 8. The method of claim 1, further including the step ofusing the object data and subset data to generate reports showing atleast one of the number of objects within a predefined area, a number ofobjects within the predefined area during a specific time period, anumber of predefined areas that were visited by an object, or dwelltimes for one or more predefined areas.
 9. The method of claim 1,further including the step of using path data, object data, and subsetdata to aggregate the most common paths taken by objects and correlatepath data information with dwell times.
 10. The method of claim 9,further including the step of generating a report that shows at leastone of the following: (a) the most common paths that objects take in astore, including corresponding dwell times; (b) changes in shoppingpatterns by time period or season; and (c) traffic patterns for use bystore security or HVAC systems in increasing or decreasing resources atparticular times.
 11. A method for counting and tracking defined objectsand anonymous objects, comprising the steps of: capturing object datawith a sensor, wherein the object data is associated with both definedobjects and anonymous objects that pass an entrance; receiving subsetdata with a data capturing device, wherein the subset data is associatedwith defined objects and includes a unique identifier, an entry time, anexit time, and location data for each defined object; receiving objectdata and subset data at a counting system; identifying matching objectand subset data, wherein matches are representative of the definedobjects; counting the defined objects and the anonymous objects;tracking the defined objects and the anonymous objects; associating alocation of a defined object with a predefined area; generating pathdata by plotting X and Y coordinates for the defined object within thepredefined area at sequential time periods; and receiving location dataat the capturing device, wherein the location data is received fromtracking technology that detects signals emitted from one of the groupconsisting of mobile handsets, membership cards, employee badges, railtickets, air tickets, rental car keys, hotel keys, store-sponsoredcredit cards, debit cards, or loyalty reward cards and wherein thesignals include T-IMSI, CDMA, or Wi-Fi signals.
 12. The method of claim11, further including the step of receiving data from anotherindependent system regarding characteristics of the object, wherein theother independent system is selected from the group consisting of pointof sale systems, loyalty rewards systems, point of sale triggerinformation, and mechanical turks.
 13. The method of claim 11, furtherincluding the step of using Z coordinates to further track objectswithin a predefined area defined by multiple floors.
 14. A method forcounting and tracking defined objects and anonymous objects, comprisingthe steps of: capturing object data with a sensor, wherein the objectdata is associated with both defined objects and anonymous objects thatpass an entrance; receiving subset data with a data capturing device,wherein the subset data is associated with defined objects and includesa unique identifier, an entry time, an exit time, and location data foreach defined object; receiving object data and subset data at a countingsystem; identifying matching object and subset data, wherein matches arerepresentative of the defined objects; counting the defined objects andthe anonymous objects; tracking the defined objects and the anonymousobjects; associating a location of a defined object with a predefinedarea; generating path data by plotting X and Y coordinates for thedefined object within the predefined area at sequential time periods;and generating a conversion rate by (a) loading transaction data relatedto transactions performed, (b) loading traffic data including a trafficcount and sorting by time periods, and (c) dividing the transactions bythe traffic counts for the time periods.
 15. The method of claim 14,wherein the transaction data includes a sales amount, the number ofitems purchases, the specific items purchased, the data and time of thetransaction, the register used for the transaction, and the salesassociate that completed the transaction.
 16. The method of claim 14,further including the step of generating a report showing comparisonsbetween purchasers and non-purchasers based on at least one of the dwelltimes, the predefined areas, or the time periods.
 17. A method forcounting and tracking defined objects and anonymous objects, comprisingthe steps of: capturing object data with a sensor, wherein the objectdata is associated with both defined objects and anonymous objects thatpass an entrance; receiving subset data with a data capturing device,wherein the subset data is associated with defined objects and includesa unique identifier, an entry time, an exit time, and location data foreach defined object; receiving object data and subset data at a countingsystem; identifying matching object and subset data, wherein matches arerepresentative of the defined objects; counting the defined objects andthe anonymous objects; tracking the defined objects and the anonymousobjects; associating a location of a defined object with a predefinedarea; generating path data by plotting X and Y coordinates for thedefined object within the predefined area at sequential time periods;defining predefined areas and generating track records for an objectincluding an enter time and an exit time for each predefined area; anddetermining a dwell time for an object within a predefined area bysubtracting the exit time for that predetermined area from the entertime for that predetermined area.
 18. The method of claim 17, furtherincluding the step of using Z coordinates to further track objectswithin a predefined area defined by multiple floors.
 19. The method ofclaim 17, further including the step of using the object data and subsetdata to generate reports showing at least one of the number of objectswithin a predefined area, a number of objects within the predefined areaduring a specific time period, a number of predefined areas that werevisited by an object, or dwell times for one or more predefined areas.20. The method of claim 17, further including the step of using pathdata, object data, and subset data to aggregate the most common pathstaken by objects and correlate path data information with dwell times.